Large Scale Image Completion via Co-Modulated Generative Adversarial Networks
Abstract
Numerous task-specific variants of conditional generative adversarial networks have been developed for image completion. Yet, a serious limitation remains that all existing algorithms tend to fail when handling large-scale missing regions. To overcome this challenge, we propose a generic new approach that bridges the gap between image-conditional and recent modulated unconditional generative architectures via co-modulation of both conditional and stochastic style representations. Also, due to the lack of good quantitative metrics for image completion, we propose the new Paired/Unpaired Inception Discriminative Score (P-IDS/U-IDS), which robustly measures the perceptual fidelity of inpainted images compared to real images via linear separability in a feature space. Experiments demonstrate superior performance in terms of both quality and diversity over state-of-the-art methods in free-form image completion and easy generalization to image-to-image translation. Code is available at https://github.com/zsyzzsoft/co-mod-gan.
Cite
Text
Zhao et al. "Large Scale Image Completion via Co-Modulated Generative Adversarial Networks." International Conference on Learning Representations, 2021.Markdown
[Zhao et al. "Large Scale Image Completion via Co-Modulated Generative Adversarial Networks." International Conference on Learning Representations, 2021.](https://mlanthology.org/iclr/2021/zhao2021iclr-large/)BibTeX
@inproceedings{zhao2021iclr-large,
title = {{Large Scale Image Completion via Co-Modulated Generative Adversarial Networks}},
author = {Zhao, Shengyu and Cui, Jonathan and Sheng, Yilun and Dong, Yue and Liang, Xiao and Chang, Eric I-Chao and Xu, Yan},
booktitle = {International Conference on Learning Representations},
year = {2021},
url = {https://mlanthology.org/iclr/2021/zhao2021iclr-large/}
}